Instead of using negative sampling or data augmentation, some works looked for other information to construct contrastive pairs, which inspired our work to consider the characteristics of the encoder.
GMI [1]}, InfoGraph [2]} do not require explicit data augmentation, the former maximizes the mutual information of edge and node features on the original graph, and the latter constructs contrastive pairs in a batch-wise fashion.
BGRL [3]} is based on the BYOL [4]} framework without the use of negative samples, which avoids the sampling bias [5]}.
AFGRL [6]} discovers nodes that can be treated as positive samples by considering the local structural information and global semantics of the graph, without creating an augmented graph view and negative sampling.
